Data Summarization
33 papers with code • 0 benchmarks • 2 datasets
Data Summarization is a central problem in the area of machine learning, where we want to compute a small summary of the data.
Benchmarks
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Libraries
Use these libraries to find Data Summarization models and implementations
MentSum
PJM(AEP)
Most implemented papers
Fair k-Center Clustering for Data Summarization
In data summarization we want to choose $k$ prototypes in order to summarize a data set.
apricot: Submodular selection for data summarization in Python
This paper presents an explanation of submodular selection, an overview of the features in apricot, and an application to several data sets.
Fast and Accurate Least-Mean-Squares Solvers
Least-mean squares (LMS) solvers such as Linear / Ridge / Lasso-Regression, SVD and Elastic-Net not only solve fundamental machine learning problems, but are also the building blocks in a variety of other methods, such as decision trees and matrix factorizations.
Scalability vs. Utility: Do We Have to Sacrifice One for the Other in Data Importance Quantification?
easeml/datascope
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CVPR 2021
Quantifying the importance of each training point to a learning task is a fundamental problem in machine learning and the estimated importance scores have been leveraged to guide a range of data workflows such as data summarization and domain adaption.
Streaming Submodular Maximization under a $k$-Set System Constraint
In this paper, we propose a novel framework that converts streaming algorithms for monotone submodular maximization into streaming algorithms for non-monotone submodular maximization.
CO-Optimal Transport
Optimal transport (OT) is a powerful geometric and probabilistic tool for finding correspondences and measuring similarity between two distributions.
Deuteros 2.0: Peptide-level significance testing of data from hydrogen deuterium exchange mass spectrometry
There are currently very few software packages available that offer quick and informative comparison of HDX-MS datasets and even few-er which offer statistical analysis and advanced visualization.
Understanding collections of related datasets using dependent MMD coresets
Understanding how two datasets differ can help us determine whether one dataset under-represents certain sub-populations, and provides insights into how well models will generalize across datasets.
$β$-Cores: Robust Large-Scale Bayesian Data Summarization in the Presence of Outliers
Modern machine learning applications should be able to address the intrinsic challenges arising over inference on massive real-world datasets, including scalability and robustness to outliers.
Fair and Representative Subset Selection from Data Streams
We study the problem of extracting a small subset of representative items from a large data stream.
